Machine Learning: Modeling Data Locally and Globally - Rilegato

Huang, Kai-zhu; Yang, Hai-qin; King, Irwin; Lyu, Michael

 
9783540794516: Machine Learning: Modeling Data Locally and Globally

Sinossi

Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves as roadmap in various models, but – more importantly – it also motivates a theory that can learn from data both locally and globally. This would help the researchers gain a deeper insight and comprehensive understanding of the techniques in this field. The book reviews current topics,new theories and applications.

Kaizhu Huang was a researcher at the Fujitsu Research and Development Center and is currently a research fellow in the Chinese University of Hong Kong. Haiqin Yang leads the image processing group at HiSilicon Technologies. Irwin King and Michael R. Lyu are professors at the Computer Science and Engineering department of the Chinese University of Hong Kong.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Contenuti

Introduction.- Global Learning vs. Local Learning: A Background Review.- A General Global Learning Model.- Learning Locally and Globally.- Application I: Imbalanced Learning.- Application II: Regression.- Summary.

Product Description

Machine Learning: Modeling Data Locally And Globally BY Irwin King, Kai-Zhu Huang, Hai-Qin Yang, Springer, Hardcover, 2008

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Altre edizioni note dello stesso titolo

9783642098345: Machine Learning: Modeling Data Locally and Globally

Edizione in evidenza

ISBN 10:  3642098347 ISBN 13:  9783642098345
Casa editrice: Springer-Verlag New York Inc, 2010
Brossura